基于支持向量机的高光谱遥感分类.pdf

1778 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 42, NO. 8, AUGUST 2004 Classification of Hyperspectral Remote Sensing Images With Support Vector Machines Farid Melgani, Member, IEEE, and Lorenzo Bruzzone, Senior Member, IEEE Abstract— This paper addresses the problem of the classifica- is possible to address various additional applications requiring tion of hyperspectral remote sensing images by support vector very high discrimination capabilities in the spectral domain (in- machines (SVMs). First, we propose a theoretical discussion and cluding material quantification and target detection). From a experimental analysis aimed at understanding and assessing the potentialities of SVM classifiers in hyperdimensional feature methodological viewpoint, the automatic analysis of hyperspec- spaces. Then, we assess the effectiveness of SVMs with respect tral data is not a trivial task. In particular, it is made complex by to conventional feature-reduction-based approaches and their many factors, such as: 1) the large spatial variability of the hy- performances in hypersubspaces of various dimensionalities. To perspectral signature of each land-cover class; 2) atmospheric sustain such an analysis, the performances of SVMs are compared effects; and 3) the curse of dimensionality. In the context of su- with those of two other nonparametric classifiers (i.e., radial basis function neural networks and the K-nearest neighbor classifier). pervised classification, one of the main difficulties is related to Finally, we study the potentially critical issue of applying binary the small ratio between the number of available training samples SVMs to multiclass problems in hyperspectral data